Against the backdrop of the continuous deepening of educational digital reform, national and Zhejiang provincial authorities have successively issued a series of policy documents on teachers’ artificial intelligence (AI) literacy, establishing clear standards for competency development among regional teachers. However, the current cultivation of teachers’ AI literacy generally faces practical dilemmas including “difficult policy standard implementation, homogenized training, and disconnection between competency diagnosis and targeted empowerment”. Existing literacy frameworks mostly focus on the formulation of macroscopic competency standards while lacking refined diagnostic indicators tailored to frontline teaching scenarios. Unified training models fail to match the personalized competency shortcomings of teachers across different educational stages and teaching seniority, and the one-size-fits-all curriculum system cannot support precise competency improvement. Furthermore, the separation between competency diagnosis and cultivation empowerment means that evaluation results are only used for current situation investigation, rather than mapping targeted improvement paths. Based on constructivist learning theory, technology-enhanced learning theory, and adaptive learning system theory, this study adopts literature analysis, policy analysis, Delphi method, and systematic design methods. Combined with the developmental characteristics of primary, secondary, and university teachers in Zhejiang Province and relying on internationally authoritative and mature evaluation tools, it constructs a localized three-dimensional (3D) diagnostic system for teachers’ AI literacy. This study further designs a two-stage “recognition-response” personalized empowerment intelligent agent and builds a full-cycle closed-loop improvement system covering “evaluation and diagnosis, intelligent empowerment, feedback and iteration”. This research fills the theoretical gap of diagnosis-oriented localized research on teachers’ AI literacy, innovates the integrated empowerment paradigm of “promoting learning through evaluation and verifying learning through practice”, and provides theoretical support and tool design references for the precise cultivation of teachers’ AI literacy and the implementation of educational digital transformation policies in Zhejiang Province.
References
[1] Zhang, P., Wu, D. (2025) China’s wisdom in addressing the imperatives of global education modernization in the digital age: on significance of digital education fronts 2025. Frontiers of Digital Education, 2(4), 32.
[2] Chen, H. (2024) The ethical challenges of educational artificial intelligence and coping measures: a discussion in the context of the 2024 World Digital Education Conference. Science Insights Education Frontiers, 20(2), 3263-3281.
[3] Mikeladze, T., Meijer, P. C., Verhoeff, R. P. (2024) A comprehensive exploration of artificial intelligence competence frameworks for educators: a critical review. European Journal of Education, 59(3), e12663.
[4] Kim, J. (2024) Leading teachers’ perspective on teacher-AI collaboration in education. Education and Information Technologies, 29(7), 8693-8724.
[5] Zhao, J., Li, S., Zhang, J. (2025) Understanding teachers’ adoption of AI technologies: an empirical study from Chinese middle schools. Systems, 13(4), 302.
[6] Dong, J., Mohd Rum, S. N., Kasmiran, K. A., Mohd Aris, T. N., Mohamed, R. (2022) Artificial intelligence in adaptive and intelligent educational system: a review. Future Internet, 14(9), 245.
[7] Fassbender, W. J. (2025) Of teachers and centaurs: Exploring the interactions and intra-actions of educators on AI education platforms. Learning, Media and Technology, 50(3), 352-364.
[8] Barakabitze, A. A., William-Andey Lazaro, A., Ainea, N., Mkwizu, M. H., Maziku, H., Matofali, A. X., Sanga, C. (2019) Transforming African education systems in science, technology, engineering, and mathematics (STEM) using ICTs: Challenges and opportunities. Education Research International, 2019(1), 6946809.
[9] Kim, J., Lee, H., Cho, Y. H. (2022) Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069-6104.
[10] Zhong, M., Wei, L., Mo, H. (2025) Enhancing graduate AI education through practical and values-driven curriculum integration. Frontiers in Education, 10, 1630073.
[11] Zhang, S., Zhao, X., Tang, Z. (2026) A systemic measurement framework of digital literacy for pre-service teachers: development, validation, and implications for sustainable digital transformation in Southwest China. Systems, 14(6), 599.
[12] Shiri, A. (2024) Artificial intelligence literacy: a proposed faceted taxonomy. Digital Library Perspectives, 40(4), 681-699.
[13] Bai, J. (2025) Overview and summary of AI competency framework for teachers. Global Medical Education, 2(1), 47-51.
[14] Tadimalla, S. Y., Maher, M. L. (2025) AI literacy as a core component of AI education. AI Magazine, 46(2), e70007.
[15] Caena, F., Redecker, C. (2019) Aligning teacher competence frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators (Digcompedu). European Journal of Education, 54(3), 356-369.
[16] Chiu, T. K., Ahmad, Z., Çoban, M. (2025) Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667-6685.
[17] Li, Y., Cheng, H., Qin, Q. (2025) Evaluations and improvement methods of deep learning ability in blended learning. International Journal of e-Collaboration (IJeC), 21(1), 1-17.
[18] Fan, Y. C., Kuo, B. C., Wu, P. C., Liao, C. H. (2025) A model for developing AI pedagogical and content knowledge in inservice and preservice non-STEM elementary teachers. Education and Information Technologies, 1-21.
[19] Koch, M. J., Carolus, A., Wienrich, C., Latoschik, M. E. (2024) Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), e39686.
[20] Maldonado, D., Cruz, E., Torres, J. A., Cruz, P. J., Benitez, S. D. P. G. (2024) Multi-agent systems: a survey about its components, framework and workflow. IEEE Access, 12, 80950-80975.
[21] Cheng, H., Qin, Q., Deng, Z., Hong, X., Tang, W. (2024) BP neural network-enhanced system for employment and mental health support for college students. International Journal of Information and Communication Technology Education, 20(1), 1-19.
[22] Chuang, S. (2021) The applications of constructivist learning theory and social learning theory on adult continuous development. Performance Improvement, 60(3), 6-14.
[23] Kyza, E. A. (2023) Technology-enhanced learning: a learning sciences perspective. Learning, Design, And Technology: An International Compendium of Theory, Research, Practice, And Policy, 221-244.
[24] Barbosa, P. L. S., Carmo, R. A. F. D., Gomes, J. P., Viana, W. (2024) Adaptive learning in computer science education: a scoping review. Education and Information Technologies, 29(8), 9139-9188.
[25] Mondal, S., Das, S., Vrana, V. G. (2023) How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), 44.
Share and Cite
Ren, T., Hong, Y., Gong, Y., Yang, Y. (2026) Theoretical Research on Personalized Empowerment Intelligent Agent for Teachers’ AI Literacy Based on Competency Diagnosis. Global Education Bulletin, 3(2), 56-71. https://doi.org/10.71052/grb2025/ESJG4926